Drive fatigue is one of the leading causes of accidents globally and one of the most neglected aspects of road safety. In this project, the most relevant and latest techniques of machine learning are utilized for developing and deploying a robust system for sleepiness recognition. The technology seeks to monitor signs of driver tiredness in real time while there is a long-distance road or travel. The proposed approach consists of using facial feature extraction to track the driver state in conjunction with vehicle data. The system mainly consists of face detection, eye tracking, and alarm system. The efficacy of this system in decreasing drowsiness- related accidents shows what a key tool this can be for improving road safety.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Drowsiness Detection System Using OpenCV


    Beteiligte:
    K K, Sivanessh (Autor:in) / Sai M, Vikram (Autor:in) / D, Saranya (Autor:in)


    Erscheinungsdatum :

    28.03.2025


    Format / Umfang :

    606412 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Driver Drowsiness Detection System with OpenCV & Keras

    Srivastava, Mayank / Idrisi, Shoyab Alam / Gupta, Tushar | IEEE | 2021


    Driver Drowsiness Detection System with OpenCV and Keras

    Fathima, R Syed Ali / Keerthi, Kovi Venkata / Bhuvanesh, Kovuri Naga et al. | IEEE | 2024


    Real-Time Driver Drowsiness Detection Using Dlib And openCV

    Singh, Prashant / Chauhan, S P S / Rajesh, E. | IEEE | 2022


    A Real-Time Driver Drowsiness Detection Using OpenCV, DLib

    Bajaj, Srinidhi / Panchal, Leena / Patil, Saloni et al. | Springer Verlag | 2022


    Drowsiness Detection System Using OpenCV and Raspberry Pi: An IoT Application

    Urunkar, Abhijeet A. / Shinde, Aditi D. / Khot, Amruta | Springer Verlag | 2022